Bibliographic record
Abstract
Chapter 2 showed that the social situation within which practices are enacted generates opportunities and places constraints on the struggle for competence. But the working consensus that grounds interaction orders is not the only force that organizes the emergence of pecking orders in practice. In Part II of the book, I want to show that social structures are also located within ourselves. We generally think of structures as somehow floating above our heads. And yet, building on Goffman, I theorize the micro-mechanism of the pecking order as the “sense of place.” As a basic diplomatic skill, the sense of place allows delegates to figure out the distribution of ranks and roles around the multilateral table. This practical feeling is both constraining – as a sense of limits – and enabling – as a display of socially recognized competence. Take, for instance, the advice that Colin Keating (an experienced former New Zealand diplomat) gives to incoming small elected members of the UN Security Council. In order to exert some influence, he writes, a competent delegation from a small country “absolutely shuns grandstanding.” For Keating, standing may be achieved by smaller delegations by striking a delicate balance between taking initiatives and contributing to the debate, on the one hand, and refraining from exaggerating one's importance and role, on the other. As they struggle for practical mastery, diplomats self-regulate their moves through the sense of place. The diplomatic sense of place relates not only to individual delegates but also to the countries being represented at the table. At the dispositional level, practical mastery for the multilateral diplomat consists of skillfully embodying the state – making the most of its standing while making up for its lapses. Although some aspects of practical mastery accrue to individual practitioners, others would seem to attach to corporate actors. This is due to the basic nature of diplomacy, which is about representing states. While diplomats show unequal dexterity in working with (or around) their country's standing, when it comes to defining what counts as valued markers, they are at the receiving end of political dynamics that mostly escape their control. On the one hand, diplomats must put up with the moves that governments make, for instance in providing (or not) certain resources to the IO at hand. This theme I will explore at length in Part IV (Chapters 7 and 8).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".